Process and device for the prediction of noise contained in a received signal

ABSTRACT

Process and device for the prediction of noise contained in a received signal For predicting noise (n) contained in a received signal (u) transmitted via a transmission channel ( 2 ), it is proposed that the autocorrelation function of the received signal (u) is obtained in order to determine noise estimated values for the noise (n) contained in the received signal (u) on the basis of the autocorrelation function. On the basis of the noise-estimated values determined thereby metrics can be updated in a decider in the form of a Viterbi decoder ( 6 ), in order to make correct decisions.

The present invention relates to a process as well as a device configured accordingly for predicting, that is to say forecasting or estimating, noise contained in a received signal, whereby the present invention can be used in particular in digital receivers or transceiver modules of wireless or wire-bound transmission systems.

In modern digital receivers the use of convolutional codes in conjunction with a Viterbi decoder has become the state of the art. A significant improvement in the signal-to-noise ratio (SNR) is obtained by the coding. The Viterbi algorithm used for the decoding provides for the use of a so-called trellis diagram, whereby the trellis diagram shows state changes over time based on the symbols received with the received signal. To decode the incoming symbols so-called path metrics are computed and by evaluating the path metrics reliability information is obtained in the form of probabilities whether a received symbol is based on a transmitted “0” or on a transmitted “1”. For this purpose those state transitions which are most probable with regard to their path metrics are selected in the trellis diagram. The reliability information is finally obtained by the added up path metrics of the best “1-path” being seen in relation to the added-up path metrics-of the best “0-path”. Those paths, which define the most probable state transitions from one time-point to a following time-point, are also known as “survivor” paths. Since the Viterbi algorithm is per se the general state of the art, it will not be discussed further at this stage.

If such a trellis-coded modulation environment (TCM) is disturbed by heavily correlated noise (HCN), such as for example by a pure sine wave with relatively large amplitude, the system can no longer function, since the convolutional and/or Viterbi decoder determines the path metrics on the basis of “normal” white Gaussian noise (WGN). In reality however this pre-condition is usually not fulfilled. While in the case of minor noise influences the performance loss resulting therefrom may be tolerable, the Viterbi decoder and therefore the corresponding digital receiver de-converges if heavily correlated noise is present.

A known process to suppress the influence of heavily correlated noise, as may arise for example in the case of radio frequency interference (RFI), is the use of so-called linear noise predictors, which forecast or estimate future noise influences and/or noise values on the basis of past noise influences and/or noise values. In this case linear noise prediction uses digital filters, whose coefficients, the so-called “taps ”, are adaptively adjusted dependent on the prediction errors of the noise predictor. The aim is to optimize the coefficients of these digital filters in such a way that the average square prediction error is minimized.

A simplified block diagram of a transmission system with a digital receiver, in which such decision-directed adaptive noise prediction is used, is reproduced in FIG. 1.

Here it is assumed that a transmitted signal v is transmitted via a channel 2 from a signal source 1 of a transmitter, which results in a correspondingly distorted or disturbed signal c at the output of the channel 2 and furthermore at the input of the respective receiver is overlaid by correlated noise n, which in FIG. 1 is indicated in the form of a noise source 3 in combination with an adder 4. The received signal of the digital receiver is fed to an equaliser 5, whereby it is assumed in the following that this concerns as optimum as possible a receiving filter, which eliminates inter-symbol interference (ISI) from the received signal. The received signal u processed by the equaliser 5 is fed to a decider 6, whereby according to FIG. 1 the decider 6 comprises a so-called slicer 7 in combination with a noise predictor 8. The slicer 7 makes symbol decisions based on the received signal fed thereto, whereby the Viterbi algorithm described above is used. The slicer 7 then outputs a signal sequence v′, whereby with the aid of an adder 9 the difference between the received signal u and the signal v′ is fed to the noise predictor 8 as an input signal, which based on this signal calculates future noise values for the noise contained in the received signal u, which with the aid of a further adder 10 are subtracted before the slicer 7 from the received signal u.

The noise predictor 8 and/or the corresponding digital linear prediction filter is optimized with regard to the filter coefficients in such a way that the average square prediction error, which is defined and minimized by the difference between the noise signal actually contained in the received signal u and the noise signal estimated by the noise predictor 8.

The approach explained above on the basis of FIG. 1 to estimate the noise component in each case contained in the received signal with the aid of a noise predictor delivers good results so long as the correct prediction coefficients for the noise predictor are known. Since however the noise signal is not present and known separately in the respective digital receiver, but rather additively overlays the actual wanted signal, correct adjustment of the filter coefficients of the noise predictor, which must be adaptively matched to the noise, is problematic. This is additionally impaired because the noise predictor is embedded in a coded environment. The problem linked with this approach is therefore the way the noise can be synthesized, which substantially corresponds to the problem as to how correct decisions can be made in a coded environment.

Instead of the decision-oriented noise prediction approach described above, therefore in order to compensate for performance loss due to correlated noise when using convolutional decoders it was proposed to carry out separate noise prediction for each survivor path of the trellis diagram on the basis of the linear prediction theory, whereby the main difference to the decision-oriented noise prediction described above is to be seen in the fact that the noise is not synthesized but, in order to make a prediction, noise estimated values are incorporated in the branch and path metrics of the trellis diagram. The path metrics are thus corrected according to the noise estimated values before the following survivor paths are determined. Since for the states of a specific time-point in the trellis diagram not only the path metrics have to be updated, but also the distances of the preceding branches appertaining to the respective survivor path, the number of the linear prediction filters to be updated at each time-point corresponds to the number of states in the trellis diagram. Therefore a noise value must be predicted by linear filtering for each state in the trellis diagram.

If the branch metrics, associated with a specific state transition from a state s to a state s′ at time-point t, are designated with λ^(s,s′)(t), these are for an optimal Viterbi decoder, which works in an environment with white Gaussian noise, the so-called Euclidean distance (ED) between the symbol u(t) received at the respective time-point and the estimated symbol e^(s,s′)(t) assigned to the respective branches: λ^(s,s′)(t)=|u(t)−e ^(s,s′)(t)|²=|δ^(s,s′)(t)|²  (1) Obviously this corresponds to the noise estimated value δ^(s,s′)(t) assigned to the respective branch between the state s and the state s′, which actually would have to be laid over the received signal if the state transition s to s′ concerned was correct. These noise estimated values of paths competing with one another at previous time-points are used to forecast, that is to day predict the respective noise value for the time-point t. The state memory of a noise predictor for a trellis-state therefore corresponds to a vector of such noise estimated values. The trellis-noise predictor state memory therefore can function as a time-dependent distance matrix which contains the noise estimated values δ^(p)(t) assigned to specific paths p by the trellis diagram: $\begin{matrix} {{\Delta(t)} = \begin{bmatrix} {\delta^{0}\left( {t - 1} \right)} & {\delta^{0}\left( {t - 2} \right)} & \ldots & {\delta^{0}\left( {t - N} \right)} \\ {\delta^{1}\left( {t - 1} \right)} & {\delta^{1}\left( {t - 2} \right)} & \ldots & {\delta^{1}\left( {t - N} \right)} \\ \vdots & \vdots & ⋰ & \vdots \\ {\delta^{2^{M} - 1}\left( {t - 1} \right)} & {\delta^{2^{M} - 1}\left( {t - 2} \right)} & \ldots & {\delta^{2^{M} - 1}\left( {t - N} \right)} \end{bmatrix}} & (2) \end{matrix}$ In this case N designates the number of time-points considered in total and M the overall memory of the Viterbi coder, so that the trellis diagram contains 2^(M) states and 2^(M) competing paths.

In FIG. 2 a trellis diagram is shown as an example with four states, the branch metrics λ^(s,s′)(t) assigned to the individual branches and/or state transitions between two sequential states as well as the path metrics Υ^(s)(t) resulting therefrom, which correspond to the branch metrics accumulated in each case, being illustrated, (s,s′=0,1,2,3). The distance matrix for the trellis diagram illustrated as an example in FIG. 2 reads as follows: $\begin{matrix} {{\Delta(t)} = \begin{bmatrix} {\delta^{0,0}\left( {t - 1} \right)} & {\delta^{0,0}\left( {t - 2} \right)} \\ {\delta^{0,1}\left( {t - 1} \right)} & {\delta^{0,0}\left( {t - 2} \right)} \\ {\delta^{3,2}\left( {t - 1} \right)} & {\delta^{1,3}\left( {t - 2} \right)} \\ {\delta^{3,3}\left( {t - 1} \right)} & {\delta^{1,3}\left( {t - 2} \right)} \end{bmatrix}} & (3) \end{matrix}$ The noise estimated values can be obtained in vector form as follows by simple matrix multiplication of the distance matrix Δ(t) and the noise predictor coefficient vector {right arrow over (w)}_(n): ξ → ⁡ ( t ) = Δ ⁡ ( t ) · w → n = [ ξ 0 ⁡ ( t ) ξ 1 ⁡ ( t ) ⋮ ξ 2 M - 1 ⁡ ( t ) ] ( 4 ) This vector of the noise estimated values must be updated for each time-point of the trellis diagram and contains the noise forecasts for all competing paths of the respective preceding time-point and/or the particular previous time instance. The noise prediction and/or noise estimated value determined for each path is used to compute the noise-estimated values for the paths leading away from the respective state at the current time-point. In the case of the example for the trellis diagram with four states illustrated in FIG. 2 therefore the noise estimated value ξ⁰(t) serves as a basis for all branches, which have their origin in the state s=0, that is to say for the branches (s,s′)=(0,0) and (s,s′)=(0,1). The computation of the path metrics therefore alters as follows: γ^(s′)(t)=min<γ^(s)(t−1)+|δ^(s,s′)(t)−ξ^(s)(t)|²>  (5) It is clear from the above equation (5) how the noise estimated values determined according to equation (4) are incorporated into the computation of the corrected path metrics, whereby the pre-condition for the equation (5) is that the state transition from the state s to the state s′ is a valid branch.

At each time-point for every trellis state a decision must be made between two competing paths. For both branches a distance measurement δ^(s,s′)(t) is determined, from which a corresponding noise estimated value ξ^(s)(t) is subtracted. In the case of the trellis diagram illustrated as an example in FIG. 2 each noise estimated value for the two competing branches of a state is computed with the aid of a second order linear filter operation, that is to say with the aid of a linear filter with two taps, whereby the same tap weights are used in each case and only the state memory for computing the noise estimated values for each state alters in each case. The state memory of the linear filters then corresponds exactly to a line of the distance matrix. The matrix multiplication is therefore realized by sequential linear filtering. The distance matrix according to equation (3) is updated as a function of all path decisions, that is to say the distance vector of the survivor path selected in each case is kept for further use, while the other distance vector is rejected. The distance vector kept is updated by the distance measurement associated with the respective survivor path, while the oldest distance measurement in this distance vector is rejected. Since assuming a state of at least two state transitions and/or branches the noise estimated value ξ^(s)(t) of a state is used at least twice. It is therefore advisable to employ the butterfly structure of the trellis diagram.

As described above on the basis of equation (4), the noise estimated values, which finally serve to compute the noise-compensated path metrics of the respective trellis diagram can be determined using the vector {right arrow over (w)}_(n), which comprises the coefficients of the noise predictor, so that in the following this vector is designated as a noise predictor coefficient vector. The fundamental problem with this approach is the correct determination of the noise predictor coefficients incorporated in this vector, in order subsequently to be able to determine the noise estimated values for computing the corrected path metrics according to equations (4) and (5).

Therefore the object of the present invention is to provide a process as well as a device configured accordingly for predicting noise contained in a received signal, in which case such noise predictor coefficients can be simply and reliably determined for a noise predictor predicting the noise contained in the received signal, wherein the invention should in particular also facilitate noise prediction in the case of heavily correlated noise.

This object is achieved according to the invention by a process for predicting noise contained in a received signal with the features of claim 1 and/or a device for predicting noise contained in a received signal with the features of claim 28. The sub-claims in each case define preferred and advantageous embodiments of the present invention.

Contrary to existing solutions with un-coded transmission methods no decision-back-coupled training of a noise predictor (which in the case of coded systems only functions by tracking the respective interference) is carried out according to the invention, but the statistics of the received signal are used at the input of the decider and/or decoder of the respective digital receiver, in order to determine the noise predictor coefficients and/or noise estimated values. For this purpose the autocorrelation function of the received signal is determined, in which the noise predictor coefficients can be directly determined as a solution of the Wiener-Hopf equation from the autocorrelation function of the received signal by suitable reworking. On the basis of the noise predictor coefficients determined in this way and the noise estimated values derived therefrom, using the equations (4) and (5) described above, noise-compensated path metrics can be computed in the decider in order, even in the presence of heavily correlated noise, to be able to make correct decisions concerning the value of the symbol received in each case.

The autocorrelation function of the received signal can be directly computed from the received signal using a suitably configured digital autocorrelation filter.

Alternatively it is also possible that the autocorrelation function of the received signal is obtained using a digital prediction filter, whereby prediction-values produced by the filter based on the received signal are subtracted from the received signal in order, dependent on the difference signal resulting therefrom, which corresponds to the prediction error of this digital filter, to adjust the filter coefficients of the filter. In a stable state of this control loop the filter coefficients of the filter contain the desired correlation information of the received signal, so that the autocorrelation function of the received signal can be derived from the filter coefficients, in particular making use of the Wiener-Hopf equation, which is also solved with the aid of the “reversive” Levinson-Durbin algorithm. A prediction filter of this kind is for example an ALE (“adaptive line enhancer”) filter.

In order to determine the noise predictor coefficients in particular from the autocorrelation function of the received signal, the autocorrelation function of the noise signal contained therein is obtained, this being possible in principle by subtracting the autocorrelation function of the wanted signal from the auto correlation function of the received signal. The autocorrelation function of the wanted signal for τ=0 in this case corresponds to the signal power of the received wanted signal, so that to compute the autocorrelation function of the noise signal only the signal power of the wanted signal must be subtracted from the autocorrelation function of the received signal for the time value and/or “lag” τ=0. For τ≠0 the autocorrelation function of the noise signal corresponds to the autocorrelation function of the received signal. The transmitting power can be simply determined dependent on the method of modulation selected in each case and is usually a constant, since scaling of the wanted signal is defined at the input of a (TCM)—convolutional decoder.

From the autocorrelation function of the noise signal, the noise predictor coefficients can be determined by solving the Wiener-Hopf equation, wherein this equation can be efficiently solved again using the Levinson Durbin algorithm due to the Toeplitz structure of the matrices.

As a rule the received wanted signal is uncorrelated, so that the process described generally always functions. There is however one transmission method which represents a special case, in which the wanted signal is only uncorrelated after a non-linear operation. This transmission method was introduced by Tomlinson and Harashima and in its principle transforms the decision-back-coupled equaliser filter into the transmitter. As a result pre-equalisation of the transmitted signal is obtained, which is again equalised by the transmission channel. The impulse response (after the decider tap) of the channel in this case corresponds to the impulse response of the pre-equaliser filter. In order to keep the pre-equaliser stable, non-linear operation (the modulo) is used. Therefore the output of the pre-equaliser is uncorrelated and has uniform distribution. If this signal is now subjected to convolution with the channel impulse response, naturally correlation of the received wanted signal results based on the channel impulse coefficients. This is irrelevant for the actual subsequent processing of the received wanted signal, since an inverse non-linear operation (again the modulo) is applied beforehand. The noise correlation can no longer be measured directly in the signal after this modulo operation. Therefore the process according to the invention can only be applied to the signal before this modulo operation. Here however the wanted signal itself is correlated and therefore the reworking must be matched to the autocorrelation function of the received signal in order to determine the autocorrelation function of the noise signal. For this purpose there are two options.

The first option consists of subtracting the entire autocorrelation function of the received wanted signal from the autocorrelation function of the received signal. This is possible, since the auto correlation function of the wanted signal can be determined a-priori by the coefficients in the pre-equaliser, e.g. by self-convolution. This process functions without limitation so long as the difference in the amount between the autocorrelation function of the noise signal and the autocorrelation of the received wanted signal is not too great.

A second option is particularly suitable, if one or more narrow-band sources of radio noise (in the special case sine sources of radio noise) are present as heavily correlated noise. In order to take into consideration such heavy correlation, advantageously in particular only the autocorrelation function of the received signal can be evaluated in a lag section, which substantially entirely contains the noise information of the respective noise signal, whereby this noise information comprises both amplitude information as well as frequency information of the noise signal. This is possible since in particular sine sources of radio noise exhibit an autocorrelation function, which with a specific amplitude corresponds to a cosine of the same frequency—thus is periodic and does not fade away. Since the autocorrelation function of the received signal with low lag values is relatively heavily overlaid by the correlation of the received data signal, this however reducing with increasing lags, it is advisable only to evaluate the autocorrelation function of the received signal for the lags τ≧ a specific limit value. The noise signal correlation information is separated by this process from the wanted signal received. If however the autocorrelation function of the narrow-band source of radio noise (e.g. sine sources of radio noise) is scanned with higher lags, a phase offset for the periodic autocorrelation function is obtained.

In order to take this phase offset into consideration the evaluated section of the autocorrelation function of the received signal can advantageously again be subjected to an autocorrelation function operation, which corresponds to a convolution of the evaluated section of the autocorrelation function of the received signal. After subsequent scaling of the autocorrelation function resulting therefrom the values of the autocorrelation function can be used as described for determining the noise predictor coefficients.

The present invention is suitable for reliable noise prediction even in the presence of heavily correlated noise for example in xDSL receivers, without however being restricted to this preferred scope of application. Rather the invention can be used in all digital receivers of wire-bound or wireless transmission systems, whereby using noise predictor coefficients noise estimated values are determined for a decider, so that the decider can make a correct decision based on these noise estimated values concerning the symbols received in each case.

The process according to the invention is based on the autocorrelation function of the received signal, which can be converted by suitable reworking into the autocorrelation function of the noise signal. This is then used by solving the Wiener-Hopf equation, preferably with the known Levinson-Durbin algorithm, to determine the predictor filter coefficients. The autocorrelation function of the received signal in this case can be determined indirectly, as illustrated according to the invention, by adapting an ALE filter and subsequent computation of the autocorrelation function, preferably with the known (inverse) Levinson-Durbin algorithm, by directly determining the autocorrelation function of the received signal with a correlator according to the prior art.

The present invention is described below in detail with reference to the accompanying drawing:

FIG. 1 shows a block diagram of a digital receiver according to the prior art with decision-oriented adaptive noise prediction,

FIG. 2 shows by way of example a trellis diagram to explain the determination of path metrics using noise-estimated values, as also proposed according to the invention,

FIG. 3 shows a block diagram of a digital receiver according to an embodiment of the present invention with an ALE filter for determining the autocorrelation function of a received signal,

FIG. 4 shows a block diagram of a digital receiver according to a further embodiment of the present invention with a digital autocorrelation filter for directly determining the autocorrelation function of a received signal from the received signal, and

FIG. 5 shows an illustration of autocorrelation functions to explain reworking of the autocorrelation function of a received signal proposed according to the invention in accordance with a preferred embodiment for determining noise predictor coefficients.

As has already been described above, the use of decision-oriented adaptive noise prediction as explained on the basis of FIG. 1 is problematic, with heavily correlated noise. The main problem in this case is that wrong decisions due to the heavily correlated noise lead to false prediction errors and thus to divergence of the linear prediction filter.

In the context of the present invention it is therefore proposed to directly evaluate the statistics of the received signal in order to determine a set of linear noise predictor coefficients. In particular in the context of the present invention it is proposed to obtain the autocorrelation function of the received signal in order to determine therefrom the autocorrelation of the noise signal, from which the noise predictor coefficients can be computed, whereby various embodiments are possible for determining the autocorrelation function of the received signal as well as the autocorrelation function of the noise signal.

In FIG. 3 a digital transmission system is illustrated similar to the transmission system shown in FIG. 1, wherein on the receiver side a device is provided according to a first embodiment for determining the autocorrelation function of the received signal.

The transmission system shown in FIG. 3 comprises a transmitter 1 with a signal source, which produces a transmitted signal v and transmits this via a wire-bound or a wireless channel 2 to a receiver, wherein the signal c transmitted via the channel 2 and consequently distorted is overlaid at the input of the receiver with noise n, which is indicated schematically in FIG. 3 in the form of a noise source 3 and an adder 4. The received signal overlaid with noise is fed to a signal equaliser 5, whose primary object is to remove intersymbol interference, wherein the equalised received signal u, which still contains a noise component, is fed to a decider 6, in which for example a Viterbi decoder is integrated. The decider b evaluates the equalised received signal u, in order for each received symbol to make as correct a decision as possible about whether the respective symbol is based on a transmitted “0” or a transmitted “1”. In the ideal case the decider 6 consequently outputs the correctly decoded transmitted signal v′.

The decider 6 makes its decisions as described above on the basis of FIG. 2 by applying the Viterbi algorithm, whereby in particular the path and/or branch metrics evaluated in connection with the respective trellis diagram are corrected in agreement with the above equations (4) and (5) by noise estimated values. In order to make this correction, the decider 6, as evident from equation (4), needs the vector {right arrow over (w)}_(n), which includes the coefficients for facilitating noise prediction, which in the following are designated as noise predictor coefficients.

In the case of the embodiment illustrated in FIG. 3 a device 12 is provided, which determines the autocorrelation function of the received signal u described below in detail and derives the noise predictor coefficients therefrom and feeds these to the decider 6, so that this can update the path and/or branch metrics in agreement with the equations (4) and (5) on the basis of these noise predictor coefficients and/or the corresponding noise prediction vector, in order to make a correct decision between “1” and “0” symbols despite the presence of possibly heavily correlated noise.

In the case of the embodiment illustrated in FIG. 3 the equalised received signal u is not only fed to the decider 6, but also directly to a filter arrangement 13, which can be designated as an adaptive “line enhancer” (ALE) filter arrangement. This filter arrangement 13 substantially comprises a digital ALE filter 14, which in principle involves a prediction error filter.

The principles of ALE filters can be taken for example from James R. Zeidler, “Performance Analysis of LMS Adaptive Prediction Filters”, Proceedings of IEEE, vol. 28, no. 12, December 1990, so that reference is made here to this publication for additional information. In the following only the main aspects, which are important for the present invention, will be reproduced.

As is evident from FIG. 3, the received signal u is fed via a delay element 15 to the ALE filter 14. Furthermore the received signal u is fed to an adder 16, which subtracts the output signal produced by the ALE filter 14 therefrom. The ALE filter 14 produces prediction values based on the input signal of the ALE filter arrangement 13 as an output signal, whereby the tap weights {right arrow over (w)}_(e),of the ALE filter arrangement 13 dependent on the tap weights {right arrow over (w)}_(f) of the digital ALE filter 14 arranged in the forward path may be illustrated as follows: $\begin{matrix} {{\overset{\rightarrow}{w}}_{e} = \begin{bmatrix} 1 \\ {- {\overset{\rightarrow}{w}}_{f}} \end{bmatrix}} & (6) \end{matrix}$ The equation (6) indicates that the prediction values produced by the ALE filter 14 based on the input signal of the ALE filter arrangement 13 are subtracted from the input signal itself, so that the output signal of the adder 16 corresponds to the prediction error. The prediction error power P_(M), of such an ALE filter arrangement 13 of M order may be expressed as follows: $\begin{matrix} {\begin{bmatrix} P_{M} \\ \overset{\rightarrow}{0} \end{bmatrix} = {\begin{bmatrix} {r(o)} & {\overset{\rightarrow}{r}}^{T} \\ \overset{\rightarrow}{r} & R \end{bmatrix}\begin{bmatrix} 1 \\ {- {\overset{\rightarrow}{w}}_{f}} \end{bmatrix}}} & (7) \end{matrix}$ In this case {right arrow over (r)} designates the autocorrelation vector with $\begin{matrix} {\overset{\rightarrow}{r} = \begin{bmatrix} {r(1)} \\ {r(2)} \\ \vdots \\ {r(M)} \end{bmatrix}} & (8) \end{matrix}$ R designates the autocorrelation matrix, which always has a Toeplitz structure: $\begin{matrix} {R = \begin{bmatrix} {r(0)} & {r(1)} & \ldots & {r\left( {M - 1} \right)} \\ {r(1)} & {r(0)} & \ldots & {r\left( {M - 2} \right)} \\ \vdots & \vdots & ⋰ & \vdots \\ {r\left( {M - 1} \right)} & {r\left( {M - 2} \right)} & \ldots & {r(0)} \end{bmatrix}} & (9) \end{matrix}$ {right arrow over (w)}_(f) is finally the vector with the linear prediction coefficients and/or prediction tap weights of the ALE filter 14; $\begin{matrix} {{\overset{\rightarrow}{w}}_{f} = \begin{bmatrix} w_{1} \\ w_{2} \\ \vdots \\ w_{M} \end{bmatrix}} & (10) \end{matrix}$ The coefficients of the ALE filter 14 are continually updated dependent on the prediction error e using the LMS algorithm, in order thus to minimize the prediction error e. As described in the publication mentioned above, the prediction error signal e in the stable state, that is to say if the description error e is minimized, does not correlate with the prediction performance P_(M). The coefficients of the ALE filter 14 in the stable state therefore contain the entire correlation information of the input signal, that is to say according to FIG. 3 of the equalised received signal u, i.e. the autocorrelation function of the received signal u can be derived in the stable state of the ALE filter arrangement 13 from the coefficients of the ALE filter 14.

In the known way the optimum prediction coefficients and/or prediction tap weights of the ALE filter 14 can be determined by solving the Wiener-Hopf equation as follows from the autocorrelation matrix R and the autocorrelation vector {right arrow over (r)}: R{right arrow over (w)}₀={right arrow over (r)}  (11) In this case {right arrow over (w)}_(o) designates the vector with the optimum prediction coefficients and/or tap weights of the ALE filter 14. Since the autocorrelation matrix and the autocorrelation vector both contain one and the same information, the exact autocorrelation function of the input signal can be computed from the vector {right arrow over (w)}_(o), whereby the following matrix equation if to be solved: $\begin{matrix} {{{\begin{bmatrix} {r(0)} & {r(1)} & \ldots & {r\left( {M - 1} \right)} \\ {r(1)} & {r(0)} & \ldots & {r\left( {M - 2} \right)} \\ \vdots & \vdots & ⋰ & \vdots \\ {r\left( {M - 1} \right)} & {r\left( {M - 2} \right)} & \ldots & {r(0)} \end{bmatrix}\begin{bmatrix} w_{1} \\ w_{2} \\ \vdots \\ w_{M} \end{bmatrix}} - \begin{bmatrix} {r(1)} \\ {r(2)} \\ \vdots \\ {r(M)} \end{bmatrix}} = 0} & (12) \end{matrix}$ Here it will not be discussed in too great detail how the above equation (12) can be solved. In the context of the present invention it is only of importance that a solution to the equation (12) exists which corresponds to the autocorrelation function of the received signal u. At this stage reference can be made additionally that a set of equations of this kind can preferably and efficiently be solved with the aid of the known reversive Levinson-Durbin reversion.

The device 12 shown in FIG. 3 not only determines the autocorrelation function of the received signal u from the coefficients of the ALE filter 14, on the contrary it is also the object of the device 12 to extract from the autocorrelation function of the received signal the autocorrelation information of the noise signal n contained therein in order to finally determine the noise predictor coefficients and transmit these on the basis of this information to the decider, which, based on these noise predictor coefficients, can finally decide the noise estimated values for updating the path and branch metrics. The noise predictor coefficients are subsequently advantageously adaptively kept or adjusted.

In this connection it can be shown that the autocorrelation function r_(u)(τ) of the received signal is constituted by the sum of the autocorrelation functions of the noise signal r_(n)(τ) and the transmission signal r_(v)(τ). For determining the autocorrelation function r_(n)(τ) of the noise component only the autocorrelation function r_(v)(τ) of the transmission signal must be subtracted from the already known autocorrelation function r_(v)(τ) of the received signal. For this purpose only a-priori knowledge of r_(v)(τ) is necessary.

For various reasons almost all communication systems comprise scrambling blocks for de-correlating the respective data, wherein the scrambling blocks are substantially necessary for the decoder algorithm. This means however that absolutely no data correlation exists between sequential data symbols, so that for the autocorrelation function of the transmission signal v the following applies: $\begin{matrix} {{r_{v}(\tau)} = \left\{ \begin{matrix} {P_{v} = {\lim\limits_{n->\infty}{\frac{1}{N}{\sum\limits_{n = 0}^{N - 1}{{v(t)}}^{2}}}}} & {\tau = 0} \\ 0 & {\tau \neq 0} \end{matrix} \right.} & (13) \end{matrix}$ In this case P_(v) designates the transmitting power of the transmitted signal v. In other words this means that only the received signal power of the wanted signal must be subtracted from the lag 0 of the autocorrelation function r_(u)(τ) of the received signal, in order to obtain the desired autocorrelation function r_(n)(τ) of the noise component as follows: $\begin{matrix} {{r_{n}(\tau)} = \left\{ \begin{matrix} {{{r_{u}(0)} - P_{v}} = {P_{u} - P_{v}}} & {\tau = 0} \\ {r_{u}(\tau)} & {\tau \neq 0} \end{matrix} \right.} & (14) \end{matrix}$ In equation (14) P_(u) designates the power of the received signal u, defined by P_(u)=r_(u)(0).

Of course it is possible to measure the received signal power of the wanted signal P_(v). This is not necessary however since usually a-priori knowledge of the received signal power of the wanted signal P_(v) is present. If for example the transmitted signal v involves a signal modulated according to M order pulse amplitude modulation, the received signal power of the wanted signal is defined as follows: $\begin{matrix} {P_{v} = {\frac{1}{3}\frac{M^{2} - 1}{M^{2}}}} & (15) \end{matrix}$ In this case only a constant value must be subtracted from r_(u)(0), in order to obtain the autocorrelation function r_(n)(τ) of the noise component.

Generally it is possible to determine the incoming power of the wanted signal irrespective of the method of modulation used in each case.

As soon as the autocorrelation function r_(n)(τ) of the noise component is known, the vector with the optimum noise predictor coefficients for the decider 6 can be determined by applying the Levinson-Durbin algorithm with the aid of the Wiener-Hopf equation: R_(n){right arrow over (w)}_(n)={right arrow over (r)}_(n)  (16) R_(n) designates the corresponding autocorrelation matrix, and {right arrow over (w)}_(n) the noise predictor coefficient vector, which contains the noise predictor coefficients for the decider 6, so that the decider 6 on the basis of these noise predictor coefficients in agreement with equation (4) can determine noise estimated values for correcting the path metrics in agreement with equation (5). For the sake of completeness it is pointed out that the Wiener-Hopf equation (15) in principle can be solved by all algorithms known from the theory of linear equation. Due to the Toeplitz structure of the autocorrelation matrix however the iterative Levinson-Durbin recursion is recommended, which is to be preferred for example with regard to its simplicity of Cholesky factorization.

FIG. 4 shows a further embodiment for determining the autocorrelation function of the received signal u, whereby the device 12 proposed according to FIG. 4 comprises a filter, which can be designated as an autocorrelation filter and the autocorrelation function is directly computed from the received signal u fed thereto. The advantage linked with this approach is that the quality of determining the autocorrelation function r_(u)(τ) can be improved, whereby the cost is also reduced in comparison to FIG. 3.

In the case of the embodiment illustrated in FIG. 4 the device 12 differs from the device shown in FIG. 3 only in the way this autocorrelation function of the received signal is determined. The autocorrelation function r_(n)(τ) of the noise signal as well as the noise predictor coefficient vector {right arrow over (w)}_(n) can be determined in similar fashion to FIG. 3.

The algorithms described above deliver perfect results if the data signal is not correlated. However applications and operating states are conceivable in which this pre-condition is not fulfilled. An example of this is the use of Tomlinson Harashima pre-coding, as described for example in M. Tomlinson, “New Automatic Equalizer Employing Modulo Arithmetic”, Electronics Letters, vol. 7, no. 576, pp. 138-139, March 1971 to which reference is made here in their full content in order to explain the principles of Tomlinson Harashima pre-coding. Due to this pre-coding the data signal is heavily correlated, this correlation heavily overlaying the autocorrelation function.

The correlation information is entirely contained in the coefficients of the Tomlinson Harashima pre-coder and therefore known to the receiver. It would in principle thus be possible to simply compute the autocorrelation function of the data signal and to subtract this from each lag of the determined and/or estimated autocorrelation function. This is possible in principle, although in practice, due to the complexity and the proneness to faults associated therewith, further improvements are necessary, in order also with a scenario of the kind described above to obtain correct information about the autocorrelation function of the received signal and/or the noise signal contained therein as a basis for determining the noise predictor coefficients, wherein the possibility also exists with a received signal dominated by the data signal correlation to obtain correct information about the autocorrelation function of the received signal and/or the noise signal contained therein.

As is known per se the autocorrelation function of a pure sine wave x(t), which in the following is briefly regarded as an example of a narrow band source of radio noise, is defined as follows: $\begin{matrix} {{x(t)} = {{{\alpha \cdot {\sin\left( {{\omega\quad t} + \phi} \right)}}->{r_{x}(\tau)}} = {\frac{a^{2}}{2}{\cos\left( {\omega\quad t} \right)}}}} & (17) \end{matrix}$ In this case r_(x)(τ) designates the autocorrelation function of the sine wave x(t), and a designates the amplitude as well as Ø the phase of the sine wave x(t). It is clear from (17) that the autocorrelation function of a sine/cosine wave is again a cosine wave with the amplitude a²/2, in which the autocorrelation function has the same frequency as the base signal, but no phase offset. The autocorrelation function is therefore not phase-dependent and contains the correlation information based on the periodic characteristics of the autocorrelation function also in sections of any arbitrarily large lags. It is therefore basically possible to derive the information pertinent for determining the noise predictor coefficients from lags of the autocorrelation function of the received signal, which are not impaired by the relatively heavy data signal correlation.

This will be explained below in detail with reference to FIG. 5, whereby in FIG. 5 as an example the sequence of the autocorrelation function of the received signal c(t) not overlaid with noise is illustrated by white circles, while additionally in FIG. 5 the autocorrelation function of the noise signal n(t) is illustrated with black circles and the autocorrelation function of the received signal u(t) is illustrated with crosses. From FIG. 5 it is particularly clear that the noise information in the autocorrelation function of the received signal contained in the autocorrelation function of the noise signal n(t) is substantially overlaid by the correlation of the data or wanted signal in the low lag section, that is to say, in the section 0≦τ≦10 the autocorrelation function of the received signal is substantially dominated by the data and/or wanted signal correlation. Furthermore it is also clear from FIG. 5 that for lags τ≦10 the influence of the data signal correlation virtually disappears, since then the sequence of the autocorrelation function of the received signal substantially corresponds to the sequence of the autocorrelation function of the noise signal. If the lags of the autocorrelation function of the received signal evaluated for determining the noise predictor coefficients are selected to be sufficiently large, these contain the entire amplitude and the frequency information of the noise signal. In order to determine the noise predictor coefficients therefore a section of the autocorrelation function of the received signal, which is no longer dominated and/or affected by the data signal correlation, can be evaluated, wherein this applies for example in the case of the sequence of the autocorrelation function shown in FIG. 5 for the section of the autocorrelation function of the received signal between lags 15 and 25.

The only problem here still to be solved is that by definition the auto correlation function of a sine wave is a cosine wave with phase 0 in the case of lag 0, that is to say where τ=0 (see equation 17). Therefore when evaluating a section of the autocorrelation function of the received signal, which does not begin with τ=0, the phase offset resulting therefrom must be considered.

A simple possibility for resolving this phase problem is to consider the section of the autocorrelation function of the received signal, for example between lags 15 and 25 evaluated in each case, as a separate signal and to re-compute the autocorrelation function from this section of the autocorrelation function. The result is a cosine wave with the same frequency and an amplitude b²/2, wherein however the reworked autocorrelation function r′_(x)(τ) obtained thereby no longer contains phase information, that is to say the phase offset equates to 0, which corresponds to the desired result, since the autocorrelation function of the noise signal can be derived therefrom as described, whereby only the autocorrelation function r′_(x)(τ) of the autocorrelation function r_(x)(τ) must be scaled with a suitable factor F for correcting the amplitude. The scaling factor can therefore be determined as follows: $\begin{matrix} \begin{matrix} {{r_{x}(\tau)} = {{{\frac{a^{2}}{2}{\cos\left( {\omega\quad t} \right)}}->{r_{x}^{\prime}(\tau)}} = {\frac{b^{2}}{2}{\cos\left( {\omega\quad t} \right)}}}} \\ {{F\frac{b^{2}}{2}}\overset{!}{=}{{\frac{a^{2}}{2}->F} = {\frac{a^{2}}{b^{2}} = \frac{a^{2}}{2{r_{x}^{\prime}(0)}}}}} \\ {b = {\left. \frac{a^{2}}{2}\Rightarrow a \right. = {\sqrt{2b} = \sqrt{2\sqrt{2{r_{x}^{\prime}(0)}}}}}} \\ {F = {\frac{\left( \sqrt{2\sqrt{2{r_{x}^{\prime}(0)}}} \right)^{2}}{2{r_{x}^{\prime}(0)}} = {2\frac{\sqrt{2{r_{x}^{\prime}(0)}}}{2{r_{x}^{\prime}(0)}}}}} \\ {= {\frac{2}{\sqrt{2{r_{x}^{\prime}(0)}}} = \sqrt{\frac{2}{r_{x}^{\prime}(0)}}}} \end{matrix} & (18) \end{matrix}$ If the circumstances described above are transposed to the present case of the autocorrelation function r_(u)(τ) of the received signal u(t), this means that only an interesting section of this autocorrelation function must be selected for further evaluation, wherein subsequently the autocorrelation function r_(u)(τ) is again determined as described from this interesting section of the autocorrelation function r_(u)(τ) and then multiplied in agreement with equation (18) by the scaling factor F for correcting the amplitude. In this case the autocorrelation function of the interesting section of the autocorrelation function r_(u)(τ) can be determined by convolution of the interesting section by r_(u)(τ). Finally as described above the autocorrelation function r_(n)(τ) of the noise signal can be determined from the autocorrelation function r′_(u)(τ), in order again on the basis of the same to calculate the noise predictor coefficients and/or the corresponding noise predictor coefficient vector {right arrow over (w)}_(n) for the decider 6. 

1. Process for the prediction of noise contained in a received signal, comprising the following steps: (a) determining the autocorrelation function of the received signal (u), and (b) computing noise estimated values for the noise (n) contained in the received signal (u) on the basis of the autocorrelation function determined in step (a) of the received signal.
 2. Process according to claim 1, characterized in that the decision is made by way of a value of a symbol received with the received signal (u) using a trellis diagram, in which metrics of the trellis diagram are corrected as a function of the noise estimated values.
 3. Process according to claim 1, characterized in that the received signal (u) is equalized before the autocorrelation function has been determined.
 4. Process according to claim 1, characterized in that the received signal (u) is fed to an adaptive filter (14), which produces prediction values for the received signal on the basis of the received signal (u) wherein, dependent on a prediction error of the adaptive filter (14), filter coefficients of the adaptive filter (14) are adjusted, and in that the autocorrelation function of the received signal (u) is determined from the filter coefficients of the adaptive filter (14), if the adaptive filter (14) has reached a stable state.
 5. Process according to claim 4, characterized in that the filter coefficients of the adaptive filter (14) are adjusted as a function of the prediction error (e) using an LMS algorithm.
 6. Process according to claim 4, characterized in that the adaptive filter (14) is an “adaptive line enhancer” filter.
 7. Process according to claim 4, characterized in that the autocorrelation function of the received signal (u) is determined from the filter coefficients of the adaptive filter (14) according to the following equation: R{right arrow over (w)}₀={right arrow over (r)}, where {right arrow over (w)}₀ designates a vector with the filter coefficients of the adaptive filter (14) in a stable state of the filter (14), {right arrow over (r)} the autocorrelation vector of the received signal (u) and R the autocorrelation matrix of the received signal (u) on the basis of the autocorrelation vector.
 8. Process according to claim 7, characterized in that the autocorrelation function of the received signal (u) is obtained from the equation indicated using the reversive Levinson-Durbin algorithm.
 9. Process according to claim 1, characterized in that the autocorrelation function of the received signal (u) is directly determined from the received signal using an autocorrelation filter (12).
 10. Process according to claim 1, characterized in that the autocorrelation function of the noise (n) contained in the received signal (u) is determined from the auto correlation function of the received signal (u), in order to determine the noise estimated values from the autocorrelation function of the noise (n).
 11. Process according to claim 10, characterized in that the autocorrelation function of the noise (n) is determined by subtraction of the autocorrelation function of a received wanted signal, on which the received signal (u) is based, from the autocorrelation function of the received signal (u).
 12. Process according to claim 11, characterized in that the autocorrelation function of the received wanted signal is determined as follows: ${r_{v}(\tau)} = \left\{ {\begin{matrix} P_{v} & {\tau = 0} \\ 0 & {\tau \neq 0} \end{matrix},} \right.$ where r_(v)(τ) designates the autocorrelation function of the received wanted signal and P_(v) the received signal power of the received wanted signal.
 13. Process according to claim 12, characterized in that the received signal (u) is pulse amplitude-modulated, wherein to determine the autocorrelation function of the received wanted signal the received signal power P_(v) of the received wanted signal is determined dependent on a number of stages of the pulse amplitude modulation as follows: ${P_{v} = {\frac{1}{3}\frac{M^{2} - 1}{M^{2}}}},$ where M designates the number of stages of the pulse amplitude modulation.
 14. Process according to claim 12, characterized in that the received signal is modulated, and in that to determine the autocorrelation function of the received wanted signal the received signal power of the received wanted signal is determined dependent on the method of modulation.
 15. Process according to claim 11, characterized in that the autocorrelation function of the noise (n) is determined as follows: ${r_{n}(\tau)} = \left\{ {\begin{matrix} {{r_{u}(0)} - P_{v}} & {\tau = 0} \\ {r_{u}(\tau)} & {\tau \neq 0} \end{matrix},} \right.$ where r_(n)(τ) designates the autocorrelation function of the noise (n), r_(u)(τ) the autocorrelation function of the received signal (u) and P_(v) the received signal power of the received wanted signal.
 16. Process according to claim 10, characterized in that noise predictor coefficients for a noise predictor are obtained from the autocorrelation function of the noise for determining the noise-estimated values via the following equation: R_(n){right arrow over (w)}_(n)={right arrow over (r)}_(n), where {right arrow over (r)}_(n) designates the autocorrelation vector of the noise (n), R_(n) the autocorrelation matrix of the noise (n) on the basis of the autocorrelation vector and {right arrow over (w)}_(n) a vector with the noise predictor coefficients.
 17. Process according to claim 16, characterized in that the noise predictor coefficients are determined via the equation indicated using the reversive Levinson-Durbin algorithm.
 18. Process according to claim 16, characterized in that after the noise predictor coefficients have been determined these are subsequently adaptively adjusted.
 19. Process according to claim 1, characterized in that the autocorrelation function of the received signal (u) is processed before the noise estimated values are obtained from the autocorrelation function of the received signal (u) for considering a correlation of the received signal (u) by a wanted signal contained in the received signal (u), wherein the noise estimated values are determined from the processed autocorrelation function of the received signal (u).
 20. Process according to claim 19, characterized in that the autocorrelation function of the received signal (u) is processed for considering a Tomlinson Harashima pre-coding, to which a transmitted signal (v) based on the received signal (u) has been subjected.
 21. Process according to claim 19, characterized in that the autocorrelation function of the received signal (u) is processed in such a way that to determine the noise estimated values only a section of the autocorrelation function of the received signal (u) is evaluated, which received signal contains substantially entirely noise information about the noise (n).
 22. Process according to claim 21, characterized in that the section of the autocorrelation function of the received signal (u) to be evaluated is selected in such a way that it contains substantially entire amplitude information and frequency information of the noise (n).
 23. Process according to claim 21, characterized in that the section of the autocorrelation function r_(u)(τ) of the received signal (u) to be evaluated corresponds to a section of the autocorrelation function of the received signal (u), where τ≧ a specific limit value.
 24. Process according to claim 21, characterized in that when the autocorrelation function of the received signal (u) is evaluated a phase offset in the autocorrelation function of the received signal (u) is corrected in consequence of the evaluation of only the section of the autocorrelation function of the received signal (u).
 25. Process according to claim 21, characterized in that before the noise estimated values have been determined from the evaluated section of the autocorrelation function of the received signal (u) the autocorrelation function of the evaluated section of the autocorrelation function of the received signal (u) is again constituted, in order to determine the noise estimated values on the basis of this re-constituted autocorrelation function.
 26. Process according to claim 25, characterized in that the autocorrelation function of the evaluated section of the autocorrelation function of the received signal (u) is constituted in that convolution of the evaluated section of the autocorrelation function of the received signal (u) is carried out.
 27. Process according to claim 25, characterized in that before the noise estimated values have been determined the autocorrelation function of the evaluated section of the autocorrelation function of the received signal (u) is scaled with a scaling factor.
 28. Device for the prediction of noise contained in a received signal, with an autocorrelation function determination device (12) for determining the autocorrelation function of the received signal (u), and with a noise predictor device (6, 12) for determining noise estimated values for the noise (n) contained in the received signal (u) from the autocorrelation function of the received signal (u).
 29. Device according to claim 28, characterized in that the device comprises an adaptive filter (14), to which the received signal (u) is fed, wherein the adaptive filter (14) is configured for producing prediction values for the received signal on the basis of the received signal (u), and wherein the adaptive filter (14) is configured for the adaptive adjustment of its filter coefficients as a function of a prediction error of the adaptive filter (14), and in that the autocorrelation determination device (12) is configured in such a way that it derives the autocorrelation function of the received signal (u) from the filter coefficients of the adaptive filter (14), provided the adaptive filter (14) is in a stable state.
 30. Device according to claim 28, characterized in that the autocorrelation determination device (12) comprises an autocorrelation filter for directly determining the autocorrelation function of the received signal (u) from the received signal (u).
 31. Device according to claim 28, characterized in that the device is configured for executing the process according to any one of claims 1-27.
 32. Digital receiver for receiving a received signal (u) transmitted via a transmission channel (2) with a device according to claim 28 for predicting noise (n) contained in the received signal (u). 